# How to integrate Callerapi MCP with LangChain

```json
{
  "title": "How to integrate Callerapi MCP with LangChain",
  "toolkit": "Callerapi",
  "toolkit_slug": "callerapi",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/callerapi/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/callerapi/framework/langchain.md",
  "updated_at": "2026-05-12T10:04:49.906Z"
}
```

## Introduction

This guide walks you through connecting Callerapi to LangChain using the Composio tool router. By the end, you'll have a working Callerapi agent that can check if this phone number is flagged as spam, retrieve carrier and business info for a caller, show your callerapi credit usage this month through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Callerapi account through Composio's Callerapi MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Callerapi with

- [OpenAI Agents SDK](https://composio.dev/toolkits/callerapi/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/callerapi/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/callerapi/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/callerapi/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/callerapi/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/callerapi/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/callerapi/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/callerapi/framework/cli)
- [Google ADK](https://composio.dev/toolkits/callerapi/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/callerapi/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/callerapi/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/callerapi/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/callerapi/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Callerapi project to Composio
- Create a Tool Router MCP session for Callerapi
- Initialize an MCP client and retrieve Callerapi tools
- Build a LangChain agent that can interact with Callerapi
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## What is the Callerapi MCP server, and what's possible with it?

The Callerapi MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Callerapi account. It provides structured and secure access to caller identification and reputation data, so your agent can perform actions like verifying phone numbers, checking caller reputation, retrieving business and carrier details, and monitoring account usage on your behalf.
- Detailed phone number lookup: Instantly retrieve information about any phone number, including reputation, business association, carrier, and FTC complaints.
- Reputation and fraud assessment: Empower your agent to check if a phone number might be flagged for spam, robocalls, or fraud, helping you screen and validate callers.
- Business and carrier identification: Have your agent fetch in-depth business and carrier details for a given number to strengthen trust and context in communications.
- HLR (Home Location Register) checks: Enable your agent to request HLR data for deeper carrier and number status insights, including ported or roaming status.
- Account usage monitoring: Let your agent access your Callerapi user profile to monitor credit usage, monthly allocations, and remaining balance, keeping you informed about your API consumption.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CALLERAPI_DISPATCH_REPORTS_MANUALLY` | Dispatch Reports Manually | Tool to manually trigger today's spam reports webhook delivery for enterprise clients. Use when an immediate webhook dispatch of spam complaint reports is needed. This endpoint is restricted to enterprise accounts only. |
| `CALLERAPI_GET_PHONE_NUMBER_INFORMATION` | Get Phone Number Information | Tool to retrieve detailed information about a specific phone number, including reputation, business and carrier details, and FTC complaints. Use when the number is in E.164 format and set hlr=true to include HLR data (adds 1-3 seconds to response). |
| `CALLERAPI_GET_USER_INFORMATION` | Get User Information | Tool to retrieve information about the authenticated user, including email and credit usage details. Use after authentication to fetch current credits spent, monthly allocation, and credits left. |
| `CALLERAPI_LIST_WEBHOOK_SUBSCRIPTIONS` | List Webhook Subscriptions | Tool to list all webhook subscriptions for daily spam reports. Enterprise clients only. Use to retrieve all configured webhook endpoints that receive spam complaint notifications. |
| `CALLERAPI_SUBSCRIBE_DAILY_REPORTS` | Subscribe to Daily Spam Reports | Tool to subscribe to daily spam report webhooks for enterprise clients. Instead of polling, receive webhook deliveries with spam complaint data daily. Use when you want to set up automated daily reports for spam complaints. |
| `CALLERAPI_TEST_WEBHOOK` | Test Webhook | Tool to send a sample webhook payload to test your webhook endpoint integration. Use to validate webhook signature verification and endpoint configuration. Enterprise clients only. |
| `CALLERAPI_UNSUBSCRIBE_DAILY_REPORTS` | Unsubscribe from Daily Reports | Tool to unsubscribe from daily spam report webhooks. Use when you need to stop receiving daily reports at a specific webhook URL. Enterprise clients only. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Callerapi MCP server is an implementation of the Model Context Protocol that connects your AI agent to Callerapi. It provides structured and secure access so your agent can perform Callerapi operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Callerapi tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Callerapi tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
- This approach allows the agent to dynamically load and use Callerapi tools as needed
```python
# Create Tool Router session for Callerapi
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['callerapi']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['callerapi']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "callerapi-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "callerapi-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Callerapi related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Callerapi related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['callerapi']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "callerapi-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Callerapi related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['callerapi']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "callerapi-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Callerapi related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Callerapi through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Callerapi MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/callerapi/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/callerapi/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/callerapi/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/callerapi/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/callerapi/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/callerapi/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/callerapi/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/callerapi/framework/cli)
- [Google ADK](https://composio.dev/toolkits/callerapi/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/callerapi/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/callerapi/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/callerapi/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/callerapi/framework/crew-ai)

## Related Toolkits

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- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
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- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Callerapi MCP?

With a standalone Callerapi MCP server, the agents and LLMs can only access a fixed set of Callerapi tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Callerapi and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Callerapi tools.

### Can I manage the permissions and scopes for Callerapi while using Tool Router?

Yes, absolutely. You can configure which Callerapi scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Callerapi data and credentials are handled as safely as possible.

---
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